mirror of
https://github.com/freqtrade/freqtrade.git
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271 lines
9.4 KiB
Python
271 lines
9.4 KiB
Python
import logging
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from abc import abstractmethod
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from enum import Enum
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from typing import Optional
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import gym
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import numpy as np
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import pandas as pd
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from gym import spaces
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from gym.utils import seeding
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from pandas import DataFrame
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logger = logging.getLogger(__name__)
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class Positions(Enum):
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Short = 0
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Long = 1
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Neutral = 0.5
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def opposite(self):
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return Positions.Short if self == Positions.Long else Positions.Long
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class BaseEnvironment(gym.Env):
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"""
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Base class for environments. This class is agnostic to action count.
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Inherited classes customize this to include varying action counts/types,
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See RL/Base5ActionRLEnv.py and RL/Base4ActionRLEnv.py
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"""
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def __init__(self, df: DataFrame = DataFrame(), prices: DataFrame = DataFrame(),
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reward_kwargs: dict = {}, window_size=10, starting_point=True,
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id: str = 'baseenv-1', seed: int = 1, config: dict = {}):
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self.rl_config = config['freqai']['rl_config']
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self.id = id
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self.seed(seed)
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self.reset_env(df, prices, window_size, reward_kwargs, starting_point)
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self.max_drawdown = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
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self.compound_trades = config['stake_amount'] == 'unlimited'
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def reset_env(self, df: DataFrame, prices: DataFrame, window_size: int,
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reward_kwargs: dict, starting_point=True):
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self.df = df
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self.signal_features = self.df
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self.prices = prices
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self.window_size = window_size
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self.starting_point = starting_point
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self.rr = reward_kwargs["rr"]
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self.profit_aim = reward_kwargs["profit_aim"]
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self.fee = 0.0015
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# # spaces
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self.shape = (window_size, self.signal_features.shape[1] + 3)
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self.set_action_space()
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self.observation_space = spaces.Box(
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low=-np.inf, high=np.inf, shape=self.shape, dtype=np.float32)
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# episode
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self._start_tick: int = self.window_size
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self._end_tick: int = len(self.prices) - 1
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self._done: bool = False
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self._current_tick: int = self._start_tick
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self._last_trade_tick: Optional[int] = None
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self._position = Positions.Neutral
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self._position_history: list = [None]
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self.total_reward: float = 0
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self._total_profit: float = 1
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self._total_unrealized_profit: float = 1
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self.history: dict = {}
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self.trade_history: list = []
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@abstractmethod
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def set_action_space(self):
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"""
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Unique to the environment action count. Must be inherited.
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"""
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def seed(self, seed: int = 1):
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self.np_random, seed = seeding.np_random(seed)
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return [seed]
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def reset(self):
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self._done = False
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if self.starting_point is True:
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self._position_history = (self._start_tick * [None]) + [self._position]
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else:
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self._position_history = (self.window_size * [None]) + [self._position]
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self._current_tick = self._start_tick
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self._last_trade_tick = None
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self._position = Positions.Neutral
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self.total_reward = 0.
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self._total_profit = 1. # unit
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self.history = {}
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self.trade_history = []
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self.portfolio_log_returns = np.zeros(len(self.prices))
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self._profits = [(self._start_tick, 1)]
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self.close_trade_profit = []
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self._total_unrealized_profit = 1
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return self._get_observation()
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@abstractmethod
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def step(self, action: int):
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"""
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Step depeneds on action types, this must be inherited.
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"""
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return
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def _get_observation(self):
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"""
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This may or may not be independent of action types, user can inherit
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this in their custom "MyRLEnv"
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"""
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features_window = self.signal_features[(
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self._current_tick - self.window_size):self._current_tick]
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features_and_state = DataFrame(np.zeros((len(features_window), 3)),
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columns=['current_profit_pct', 'position', 'trade_duration'],
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index=features_window.index)
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features_and_state['current_profit_pct'] = self.get_unrealized_profit()
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features_and_state['position'] = self._position.value
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features_and_state['trade_duration'] = self.get_trade_duration()
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features_and_state = pd.concat([features_window, features_and_state], axis=1)
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return features_and_state
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def get_trade_duration(self):
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if self._last_trade_tick is None:
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return 0
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else:
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return self._current_tick - self._last_trade_tick
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def get_unrealized_profit(self):
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if self._last_trade_tick is None:
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return 0.
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if self._position == Positions.Neutral:
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return 0.
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elif self._position == Positions.Short:
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current_price = self.add_entry_fee(self.prices.iloc[self._current_tick].open)
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last_trade_price = self.add_exit_fee(self.prices.iloc[self._last_trade_tick].open)
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return (last_trade_price - current_price) / last_trade_price
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elif self._position == Positions.Long:
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current_price = self.add_exit_fee(self.prices.iloc[self._current_tick].open)
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last_trade_price = self.add_entry_fee(self.prices.iloc[self._last_trade_tick].open)
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return (current_price - last_trade_price) / last_trade_price
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else:
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return 0.
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@abstractmethod
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def is_tradesignal(self, action: int):
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# trade signal
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"""
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Determine if the signal is a trade signal. This is
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unique to the actions in the environment, and therefore must be
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inherited.
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"""
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return
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def _is_valid(self, action: int):
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# trade signal
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"""
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Determine if the signal is valid.This is
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unique to the actions in the environment, and therefore must be
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inherited.
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"""
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return
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def add_entry_fee(self, price):
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return price * (1 + self.fee)
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def add_exit_fee(self, price):
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return price / (1 + self.fee)
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def _update_history(self, info):
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if not self.history:
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self.history = {key: [] for key in info.keys()}
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for key, value in info.items():
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self.history[key].append(value)
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@abstractmethod
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def calculate_reward(self, action):
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"""
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Reward is created by BaseReinforcementLearningModel and can
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be inherited/edited by the user made ReinforcementLearner file.
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"""
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return 0.
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def _update_unrealized_total_profit(self):
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"""
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Update the unrealized total profit incase of episode end.
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"""
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if self._position in (Positions.Long, Positions.Short):
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pnl = self.get_unrealized_profit()
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if self.compound_trades:
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# assumes unit stake and compounding
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unrl_profit = self._total_profit * (1 + pnl)
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else:
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# assumes unit stake and no compounding
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unrl_profit = self._total_profit + pnl
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self._total_unrealized_profit = unrl_profit
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def _update_total_profit(self):
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pnl = self.get_unrealized_profit()
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if self.compound_trades:
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# assumes unite stake and compounding
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self._total_profit = self._total_profit * (1 + pnl)
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else:
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# assumes unit stake and no compounding
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self._total_profit += pnl
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def most_recent_return(self, action: int):
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"""
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Calculate the tick to tick return if in a trade.
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Return is generated from rising prices in Long
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and falling prices in Short positions.
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The actions Sell/Buy or Hold during a Long position trigger the sell/buy-fee.
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"""
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# Long positions
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if self._position == Positions.Long:
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current_price = self.prices.iloc[self._current_tick].open
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previous_price = self.prices.iloc[self._current_tick - 1].open
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if (self._position_history[self._current_tick - 1] == Positions.Short
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or self._position_history[self._current_tick - 1] == Positions.Neutral):
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previous_price = self.add_entry_fee(previous_price)
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return np.log(current_price) - np.log(previous_price)
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# Short positions
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if self._position == Positions.Short:
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current_price = self.prices.iloc[self._current_tick].open
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previous_price = self.prices.iloc[self._current_tick - 1].open
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if (self._position_history[self._current_tick - 1] == Positions.Long
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or self._position_history[self._current_tick - 1] == Positions.Neutral):
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previous_price = self.add_exit_fee(previous_price)
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return np.log(previous_price) - np.log(current_price)
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return 0
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def get_portfolio_log_returns(self):
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return self.portfolio_log_returns[1:self._current_tick + 1]
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def update_portfolio_log_returns(self, action):
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self.portfolio_log_returns[self._current_tick] = self.most_recent_return(action)
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def current_price(self) -> float:
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return self.prices.iloc[self._current_tick].open
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def prev_price(self) -> float:
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return self.prices.iloc[self._current_tick - 1].open
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def sharpe_ratio(self):
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if len(self.close_trade_profit) == 0:
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return 0.
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returns = np.array(self.close_trade_profit)
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reward = (np.mean(returns) - 0. + 1e-9) / (np.std(returns) + 1e-9)
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return reward
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